Information Theory for Data Science by Mir Hossain (.ePUB)
File Size: 15.8 MB
Information Theory for Data Science: From Entropy to Machine Learning, AI, and Modern Analytics by Mir Hossain
Requirements: .ePUB reader, 15.8 MB
Overview: Master the mathematics that powers modern Machine Learning (ML), Artificial Intelligence (AI), data analytics, and Large Language Models (LLMs). Information theory is the hidden language of Data Science. Every time a model minimizes cross-entropy loss, every time features are selected using mutual information, and every time an AI system predicts the next token, information theory is at work. Information Theory for Data Science provides a practical, modern introduction to the concepts that drive today’s data-driven technologies. Starting with the foundations of probability and information, this book builds step-by-step toward entropy, divergence measures, feature selection, Machine Learning applications, Deep Learning, Generative AI, and Large Language Models. Unlike traditional information theory texts that focus primarily on communication systems, this book emphasizes real-world applications in Data Science and AI, helping readers connect mathematical concepts directly to modern analytics and Machine Learning workflows. Whether you are a data scientist, ML engineer, AI practitioner, Computer Science student, researcher, or quantitative analyst, this book will help you develop a deep understanding of how information flows through modern intelligent systems—and how to use that knowledge to build better models and make better decisions.
Genre: Non-Fiction > Tech & Devices
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